Refinement of Machine Learning Engines for Automatically Generating Component-Based User Interfaces

ABSTRACT

Techniques are disclosed relating to refining, based on user feedback, one or more machine learning engines for automatically generating component-based user interfaces. In various embodiments, a computer system stores template information that defines a plurality of component types and one or more display parameters identified for one or more user interfaces. The computer system may receive a request to generate a user interface, where the request specifies a data set to be displayed. Further, the computer system may automatically generate a user interface, where the generating is performed by one or more machine learning engines that use the template information and the data set as inputs. The computer system may then provide the user interface to one or more users, receive user feedback associated with the user interface, and train at least one of the one or more machine learning engines based on the user feedback.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following U.S. applications filed onOct. 31, 2018: U.S. application Ser. No. ______ (Attorney Docket Number7000-17700/4061US1), U.S. application Ser. No. ______ (Attorney DocketNumber 7000-17800/4061US2), U.S. application Ser. No. ______ (AttorneyDocket Number 7000-18000/4061US4), U.S. application Ser. No. ______(Attorney Docket Number 7000-18100/4061US5), U.S. application Ser. No.______ (Attorney Docket Number 7000-18200/4061US6), and U.S. applicationSer. No. ______ (Attorney Docket Number 7000-18300/4061US7). Each of theabove-referenced applications is hereby incorporated by reference as ifentirely set forth herein.

BACKGROUND Technical Field

Embodiments described herein relate to user interface technology and, inparticular, to component-based techniques for automatically generatinguser interfaces.

Description of the Related Art

User interfaces are often generated by multiple skilled designers, e.g.,to combine quality coding techniques with graphical design to achievedesired functionality while pleasing the eye, achieving branding goals,or promoting desired user behaviors. Many entities may desire customizedinterfaces rather than using generic templates. Many entities do nothave access, however, to coding or design expertise needed to generatean effective user experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example system forautomatically generating user interface data, according to someembodiments.

FIG. 2A is a diagram illustrating example user interface code and aresultant display of an identified component, according to someembodiments.

FIG. 2B is a block diagram illustrating an example user interface withmultiple component types, according to some embodiments.

FIG. 3 is a block diagram illustrating an example grouping module forgenerating sets of component types based on existing user interfaces,according to some embodiments.

FIGS. 4A and 4B are diagrams illustrating example techniques forgrouping nearby elements of existing user interfaces into components,according to some embodiments.

FIG. 5 is a diagram illustrating a user interface and variouscorresponding depths of displayed user interface elements withinhierarchical interface code, according to some embodiments.

FIGS. 6A and 6B illustrate an unsupervised k-means clustering examplebased on coordinates and depth, according to some embodiments.

FIG. 7 is a block diagram illustrating a user interface corresponding toFIG. 5 with elements grouped into different components, according tosome embodiments.

FIG. 8 is a block diagram illustrating an example method for groupingnearby elements of a user interface into components, according to someembodiments.

FIGS. 9A and 9B are diagrams illustrating example techniques forclassifying elements within an identified component, according to someembodiments.

FIG. 10 is a table illustrating example scoring of a visible userinterface element for classification based on a plurality of metadatafield values, according to some embodiments.

FIG. 11 is a block diagram illustrating an example method forclassifying visible user interface elements based on metadata fields,according to some embodiments.

FIGS. 12A and 12B are diagrams illustrating example techniques foridentifying types of identified interface components, according to someembodiments.

FIG. 13 illustrates example classification of two user interfacecomponents by a classification module within a component typeidentification module, according to some embodiments.

FIGS. 14A and 14B illustrate example pixel characteristics forgenerating a pixel gradient, according to some embodiments.

FIG. 15 illustrates an example technique for generating pixel gradientsfor the bounding boxes of user interface elements within a component,according to some embodiments.

FIG. 16 illustrates an example technique for determining a similaritymetric for two components based on pixel matrices representinggradients, according to some embodiments.

FIG. 17 illustrates an example method for identifying types of userinterface components, according to some embodiments.

FIG. 18 is a flow diagram illustrating an example method for identifyingduplicate interface regions, according to some embodiments.

FIG. 19 is a block diagram illustrating a more detailed example of theinterface generator module shown in FIG. 1, according to someembodiments.

FIG. 20 is a diagram illustrating two example component types identifiedfor a set of input data, according to some embodiments.

FIG. 21 is a flow diagram illustrating an example component-based methodfor automatically generating a user interface, according to someembodiments.

FIG. 22 is a flow diagram for automatically generating user interfaceinformation for a predicted event, according to some embodiments.

FIG. 23 is a block diagram illustrating an example interface generationmodule, according to some embodiments.

FIG. 24 is a block diagram illustrating an example interface used tosuggest various automatically generated user interface components,according to some embodiments.

FIG. 25 is a flow diagram illustrating an example method for refiningmachine learning engines used to automatically generate component-baseduser interfaces, according to some embodiments.

FIG. 26 is a block diagram illustrating an example computing device,according to some embodiments.

This disclosure includes references to “one embodiment,” “a particularembodiment,” “some embodiments,” “various embodiments,” “an embodiment,”etc. The appearances of these phrases do not necessarily refer to thesame embodiment. Particular features, structures, or characteristics maybe combined in any suitable manner consistent with this disclosure.

Within this disclosure, different entities (which may variously bereferred to as “units,” “circuits,” other components, etc.) may bedescribed or claimed as “configured” to perform one or more tasks oroperations. This formulation—[entity] configured to [perform one or moretasks]—is used herein to refer to structure (i.e., something physical,such as an electronic circuit). More specifically, this formulation isused to indicate that this structure is arranged to perform the one ormore tasks during operation. A structure can be said to be “configuredto” perform some task even if the structure is not currently beingoperated. For example, a “module configured to select a component type”is intended to cover, for example, equipment that has a program code orcircuitry that performs this function during operation, even if thecircuitry in question is not currently being used (e.g., a power supplyis not connected to it). Thus, an entity described or recited as“configured to” perform some task refers to something physical, such asa device, circuit, memory storing program instructions executable toimplement the task, etc. This phrase is not used herein to refer tosomething intangible. The term “configured to” is not intended to mean“configurable to.” An unprogrammed FPGA, for example, would not beconsidered to be “configured to” perform some specific function,although it may be “configurable to” perform that function afterprogramming.

Reciting in the appended claims that a structure is “configured to”perform one or more tasks is expressly intended not to invoke 35 U.S.C.§ 112(f) for that claim element. Accordingly, none of the claims in thisapplication as filed are intended to be interpreted as havingmeans-plus-function elements. Should Applicant wish to invoke Section112(f) during prosecution, it will recite claim elements using the“means for” [performing a function] construct.

It is to be understood that the present disclosure is not limited toparticular devices or methods, which may, of course, vary. It is also tobe understood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting. As used herein, the singular forms “a”, “an”, and “the”include singular and plural referents unless the context clearlydictates otherwise. Furthermore, the words “can” and “may” are usedthroughout this application in a permissive sense (i.e., having thepotential to, being able to), not in a mandatory sense (i.e., must). Theterm “include,” “comprise,” and derivations thereof, mean “including,but not limited to.” The term “coupled” means directly or indirectlyconnected.

As used herein, the term “based on” is used to describe one or morefactors that affect a determination. This term does not foreclose thepossibility that additional factors may affect the determination. Thatis, a determination may be solely based on specified factors or based onthe specified factors as well as other, unspecified factors. Considerthe phrase “determine A based on B.” This phrase specifies that B is afactor used to determine A or that affects the determination of A. Thisphrase does not foreclose that the determination of A may also be basedon some other factor, such as C. This phrase is also intended to coveran embodiment in which A is determined based solely on B. As usedherein, the phrase “based on” is synonymous with the phrase “based atleast in part on.”

As used herein, the phrase “in response to” describes one or morefactors that trigger an effect. This phrase does not foreclose thepossibility that additional factors may affect or otherwise trigger theeffect. That is, an effect may be solely in response to those factors,or may be in response to the specified factors as well as other,unspecified factors. Consider the phrase “perform A in response to B.”This phrase specifies that B is a factor that triggers the performanceof A. This phrase does not foreclose that performing A may also be inresponse to some other factor, such as C. This phrase is also intendedto cover an embodiment in which A is performed solely in response to B.

As used herein, the terms “first,” “second,” etc. are used as labels fornouns that they precede, and do not imply any type of ordering (e.g.,spatial, temporal, logical, etc.), unless stated otherwise. When used inthe claims, the term “or” is used as an inclusive or and not as anexclusive or. For example, the phrase “at least one of x, y, or z” meansany one of x, y, and z, as well as any combination thereof (e.g., x andy, but not z).

DETAILED DESCRIPTION

In various disclosed embodiments, a computing system is configured toautomatically generate user interface code for input data to bedisplayed. For example, the input data may be specified via anapplication programming interface (API) without complete specificationof layout or formatting and the computing system may automatically groupand format the input data. Various techniques discussed herein arecomponent-based and map user input data to known component types andautomatically format components. A component may include multiplevisible user interface elements (e.g., images, text strings, links,etc.). In some embodiments, a machine learning engine is trained toidentify and format components based on code from prior user interfaces.

In some embodiments, the system may generate a user interface subject toone or more constraints. For example, the constraints may be based onavailable screen resolution, predicted events, style from anotherinterface, etc. In some embodiments, techniques similar to those used toidentify components on existing interfaces may also identify and reportduplicate designs (and may automatically merge such portions of theinterface).

In various embodiments, the disclosed techniques may allow entities toautomatically generate user interfaces without requiring knowledge ofdesign or coding techniques. Further, the disclosed techniques mayadvantageously improve existing user interface technology, includingautomating design tasks that were previously performed manually, in anew way (e.g., using component-based techniques). These techniques mayimprove user interface flexibility and functionality, in variousembodiments.

This disclosure initially describes, with reference to FIGS. 1-2B, asystem for automatically generating a user interface usingcomponent-based techniques and example components within an interface.Example code, elements, components, and a user interface are discussedwith reference to FIGS. 2A and 2B. Techniques for generating componenttypes based on one or more existing interfaces are discussed withreference to FIGS. 3-17. Example techniques for identifying duplicateinterface design are discussed with reference to FIG. 18.

This disclosure then describes, with reference to FIGS. 19-21, exampletechniques for automatically generating user interfaces using knowncomponent types (which may be identified using the techniques discussedwith reference to the prior figures). Techniques for automaticallygenerating an interface for predicted or speculative events arediscussed with reference to FIG. 22. Machine learning techniques withfeedback based on user activity are discussed with reference to FIGS.23-25.

Overview of System for Automatically Generating User Interfaces

FIG. 1 is a block diagram illustrating an example system forautomatically generating user interface data, according to someembodiments. In the illustrated embodiment, system 100 analyzes existinguser interface data 105, receives data to be displayed 115, andautomatically generates output user interface data 125. In theillustrated embodiment, system 100 includes visible element extractionmodule 110, grouping module 120, and interface generator module 130. Insome embodiments, system 100 also presents user interface options andreceives admin input and/or user interaction information (e.g., whichmay be used to train computer learning implementations of module 130).

Visible element extraction module 110, in the illustrated embodiment,receives data 105 from an existing user interface. This data may bespecified according to a hierarchical tree structure (e.g., usingdocument object model (DOM) techniques). In some embodiments, module 110extracts elements that may be visible in the user interface from theuser interface data 105. For example, these elements may be leaf nodesof the tree structure.

Grouping module 120, in some embodiments, is configured to group sets ofextracted elements into components. In some embodiments, this groupingis based on coordinates of the elements and depth of the elements withinthe tree. In the illustrated embodiment, module 120 provides detectedcomponent types and display parameters (e.g., formatting and/or layoutcomponents) to interface generator module 130.

Interface generator module 130, in the illustrated embodiment, isconfigured to generate user interface data 125 based on the data frommodule 120 and one or more input constraints. In some embodiments,module 130 provides one or more options, e.g., selectable by anadministrator for implementation in the interface. In some embodiments,module 130 receives information based on user interaction with theinterface, which may be used for training, for example.

Various techniques disclosed herein may be used alone or in combination.The disclosed architectures are included for purposes of illustration,but are not intended to limit the scope of the present disclosure. Forexample, various functionality described with reference to one modulemay be performed by other modules, in other embodiments.

As used herein, the term “module” refers to circuitry configured toperform specified operations or to physical non-transitory computerreadable media that store information (e.g., program instructions) thatinstructs other circuitry (e.g., a processor) to perform specifiedoperations. Modules may be implemented in multiple ways, including as ahardwired circuit or as a memory having program instructions storedtherein that are executable by one or more processors to perform theoperations. A hardware circuit may include, for example, customvery-large-scale integration (VLSI) circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A module may also be implemented in programmablehardware devices such as field programmable gate arrays, programmablearray logic, programmable logic devices, or the like. A module may alsobe any suitable form of non-transitory computer readable media storingprogram instructions executable to perform specified operations.

Note that the modules of FIG. 1 may be included in different computingsystems, in some embodiments. For example, in some embodiments a firstcomputing system may generate the component types and formatting/layoutinformation and transmit this data to a second computing system thatimplements module 130. Further, various actions discussed herein may beperformed in close time proximity or may be separated. For example, oncea set of component types and formatting/layout data has been generated,this data may be used to automatically generate other interfacespotentially indefinitely.

Example Code, Elements, Components, and User Interface

FIG. 2A is a diagram illustrating example user interface code and aresultant display of an identified component, according to someembodiments. In the illustrated embodiment, the leaf nodes (theelements) in code 210 are emphasized using dashed and dotted lines.These elements can be displayed in interface 220 (although that elementmay not always be displayed, e.g., due to input variables, userscrolling, user interaction, etc.). As indicated in FIG. 2A, elementsmay be grouped into components based on coordinates, classification,and/or depth in the interface code hierarchy.

As used herein, the term “element” refers to information that can beoutput on a user interface, e.g., visually or audibly. Elements aretypically explicitly indicated by user interface code. For hierarchicalcode such as Hypertext Markup Language (HTML), for example, elements aretypically leaf nodes. Non-limiting examples of types of elements includetext, links, images, etc.

As used herein, the term “component” refers to a group of one or moreelements. Typically, components include multiple elements. Componentsare typically not explicitly identified by traditional user interfacecode. Rather, various disclosed techniques automatically detectcomponent types for use in auto-generating user interfaces. A componenttype may define a set of elements that make up a class of components.For example, a component type may include an image, a link, and multiplelines of text. The component type may also have corresponding displayparameters that specifying formatting and layout of elements withincomponents of the type. In some embodiments, component types are used toautomatically organize user interface inputs into a user interface.

In the example of FIG. 2A, displayed interface 220 includes anidentified component 230 that includes an image element 232, a labelelement 234, and a link element 236. For example, for the code 210, theimage element 232 may be an image indicated by the <img src=“ . . . ”>line, the label 234 may display the text “John Doe” and the link 236 maybe selectable to visit www.jd.com.

FIG. 2B is a block diagram illustrating an example user interface withmultiple identified components, according to some embodiments. In theillustrated embodiment, the interface includes components A through H.In the illustrated embodiment, components B, C, and D may be identifiedas the same component type and components E and F may be identified asthe same component type. Note that the same type of component may beused to display different content, but may use the same set of elementsto do so.

Component G, in the illustrated embodiment, may include a singleelement, e.g., a background image for the interface. As shown,components may be at least partially overlapping in a display space, insome embodiments. Component A is an overall toolbar for multiple pagesof a web interface and component H is a search component. Note that thecomponents in FIG. 2B may be identified using techniques discussed infurther detail below in an existing interface. In other embodiments,component types for the illustrated embodiments may be automaticallydetermined for user interface inputs and the interface of FIG. 2B may begenerated automatically.

FIGS. 3-17, discussed in further detail below, discuss exampletechniques for determining a set of component types from one or moreexisting interfaces. Note that various techniques discussed incombination with other techniques may also be used separately and thevarious techniques disclosed herein may be combined in various differentcombinations.

Overview of Grouping Module

FIG. 3 is a block diagram illustrating an example grouping module forgenerating sets of component types based on existing user interfaces,according to some embodiments. In the illustrated embodiment, groupingmodule 120 includes clustering module 310, classification module 320,and component type identification module 330. In some embodiments, thecomponent type definitions, generated based on clustering andclassification, and component type identification techniques, are usedas training data for a machine learning system, where the systemautomatically generates one or more user interfaces. For example,interface generator module 130, discussed above with reference to FIG.1, may use component type definitions received from grouping module 120.

In the illustrated embodiment, clustering module 310 receiveshierarchical user interface code for one or more existing graphical userinterfaces (GUIs). Based on the user interface code, in someembodiments, module 310 identifies groups of nearby user interfaceelements. In some embodiments, clustering module 310 generates groups ofelements using unsupervised clustering, as discussed below withreference to FIGS. 4-8.

In some embodiments, as discussed above with reference to FIG. 1, thehierarchical user interface code is a tree structure such as a DOM tree.In some embodiments the DOM is a programming interface for an HTMLdocument that represents a website page, where each leaf node (nodesthat do not have child nodes) of the tree structure represents an outputelement of the user interface.

In the illustrated embodiment, classification module 320 receivesidentified components from clustering module 310. In the illustratedembodiment, classification module 320 classifies one or more elements inthe groups of identified components received from module 310. In theillustrated embodiment, classification module 320 sends classificationinformation for the elements in the identified components to componenttype identification module 330.

In some embodiments, component type identification module 330 calculatesa similarity metric between components based on the classification andbounding boxes of the identified components. In some embodiments, module330 generates component type definitions for sets of similar componentsthat meet a threshold similarity metric. In the illustrated embodiment,module 330 generates component type definitions.

In some embodiments, the component type definitions generated bycomponent type identification module 330 are stored in a database. Insome embodiments, the database stores input and/or training data (e.g.,component type definitions, user interaction information, formattinginformation, display parameters, etc.) for a machine learning systemthat automatically generates user interface data.

Example Techniques for Grouping User Interface Elements into Components

FIGS. 4A and 4B are diagrams illustrating example techniques forgrouping nearby elements of existing user interfaces into components,according to some embodiments. In FIG. 4A, clustering module 310receives one or more DOMs for existing user interfaces. In theillustrated embodiment, based on the DOM, module 310 identifiescomponents by grouping nearby elements. As discussed above withreference to FIG. 2A, in some embodiments, types of elements within theidentified components include one or more of the following: a label,text, an image, a link, a video, a button, a selectable shape, adrop-down list, a search input, etc. Note that certain element types(e.g., text) may include multiple other sub-types (e.g., labels, links,etc.) or types may be identified to be mutually exclusive, in someembodiments.

At element 410, clustering module 310 harvests the visible elements(e.g., leaf nodes) from the DOM of one or more existing user interfaces.In some embodiments, module 310 implements the functionality discussedabove with reference to visible element extraction module 110 of FIG. 1.

At element 412, in the illustrated embodiment, clustering module 310determines coordinates based on the bounding boxes of the visibleelements. Examples of coordinate values for bounding boxes are discussedbelow with reference to FIGS. 6A and 6B.

At element 414, in the illustrated embodiment, module 310 determinesdepth values based on element depths within the DOM (e.g., the number oflevels of the element from the root level of the DOM).

At 416, in the illustrated embodiment, module 310 performs anunsupervised clustering process. In some embodiments, the process is ak-means clustering processes. Examples of depth values and k-meansclustering are discussed in further detail below with reference to FIG.5.

At element 418, in the illustrated embodiment, module 310 groupselements based on the outputs from the unsupervised clustering. In theillustrated embodiment, clustering module 310 outputs groupinginformation for user interface elements. Examples of element groupingare discussed below with reference to FIG. 7. In some embodiments, thegrouping information generated by module 310 is used as training datafor machine learning. In some embodiments, grouping information is sentto modules 320 and 330 for classification and identification of elementswithin the groups (components).

FIG. 5 is a diagram illustrating a user interface and variouscorresponding depths of displayed user interface elements withinhierarchical interface code, according to some embodiments. In theillustrated embodiment, user interface (UI) 510 includes various visibleinterface elements, including: an image, a link, a label (“John Doe”),text (“This is the profile of John Doe”), and links A-E. The informationdisplayed in UI 510, in the illustrated embodiment, is contained in theinterface code received by clustering module 310. In some embodiments,clustering module 310 determines the depths of each of the visibleelements in the tree. In the illustrated example, the depths in the treefrom the roots are shown for the various elements displayed in UI 510.

In some embodiments, the displayed depth values are modified using anon-linear function by clustering module 310. For example, in theillustrated embodiment, the lower portion of the image illustratessquared depth values. In some embodiments, the depth values areexponentially modified by a value other than two (e.g., 1.5, 3, 4, 5,etc.). In some embodiments, the depth values are modified usingmathematical functions other than exponential functions. In someembodiments, the depth values are modified to alter the effects ofdifferences in the depth values, relative to the effects of otherclustering inputs such as coordinates of the bounding boxes of the leafelements (e.g., to emphasize the depth value over the coordinates).

In some embodiments, in addition to determining the depths of thevisible user interface elements in the DOM, the clustering module 310determines coordinates in one or more dimensions (e.g., horizontal andvertical coordinates, typically referred to as x and y coordinates) ofthe visible elements in a screen space for UI 510. Althoughtwo-dimensional bounding boxes are discussed herein for purposes ofillustration, additional dimensions may also be considered, in otherembodiments, e.g., a z-dimension in three-dimensional displays. In someembodiments, the horizontal and vertical coordinate values, in additionto the squared depth values, are stored and evaluated by clusteringmodule 310.

FIGS. 6A and 6B illustrate an unsupervised k-means clustering examplebased on coordinates and depth, according to some embodiments. FIG. 6Ais a plot of example x coordinate values, y coordinate values, andsquared depth values (stored by clustering module 310) as points in athree-dimensional space. In other embodiments, clustering may beperformed with other numbers of dimensions, e.g., for three-dimensionalinterfaces. In the illustrated graph, points have been grouped usingk-means clustering with different groups having different shades.

In the illustrated embodiment, five different k-means groups are shown.Various numbers of groupings may be produced from unsupervisedclustering. In some embodiments, the number of k-means groupings isbased on the total number (and/or locations) of elements in a userinterface. For example, if there are 100 different elements within auser interface, there may be 30 different k-means groupings for thatuser interface. In some embodiments, each group corresponds to acomponent and may be further analyzed to determine a set of componenttypes for the interface.

In FIG. 6B, a table is shown with the x, y, and depth values, along withthe resultant k-means grouping values 610 determined by unsupervisedk-means clustering for some of the interface elements from FIG. 5. Inthe illustrated embodiment, the image, the label “John Doe”, and thelink of UI 510 are grouped in k-means group 1. The text “This is theprofile of John Doe” of UI 510 is grouped in k-means group 2, in theillustrated embodiment. Note that, in some embodiments, no element isgrouped within multiple groups (e.g., the elements may not overlap ingroupings).

An unsupervised k-means clustering process may group values based ontheir relative distances from each other within the k-means graph (e.g.,their relative x, y, and z coordinates). In some embodiments, one ormore other clustering processes are used in addition to or in place ofk-means clustering, such as one or more of the following: Gaussianmixture mode, expectation-maximization, density-based spatial clusteringof applications with noise (DBSCAN), mean-shift, and/or affinitypropagation.

FIG. 7 is a block diagram illustrating a user interface corresponding toFIG. 5 with elements grouped into components, according to someembodiments. In the illustrated embodiment, groupings are shown usingdashed lines based on the k-means clustering performed for the elementsof user interface 510. In the illustrated embodiment, image 712, label714, and link 716 are grouped together based on the unsupervised k-meansclustering assigning each of these elements a k-means group value of 1.In the illustrated embodiment, text 722 is the only element in itsrespective grouping (group 2). In the illustrated embodiment, links A-E732 are grouped together in a third grouping.

FIG. 8 is a block diagram illustrating an example method for groupingnearby elements of a user interface into components, according to someembodiments. The method shown in FIG. 8 may be used in conjunction withany of the computer circuitry, systems, devices, elements, or componentsdisclosed herein, among other devices. In various embodiments, some ofthe method elements shown may be performed concurrently, in a differentorder than shown, or may be omitted. Additional method elements may alsobe performed as desired.

At 810, in the illustrated embodiment, a computing device determines,based on hierarchical user interface code, a plurality of visibleelements of a graphical user interface.

At 820, in the illustrated embodiment, the computing device determinescoordinates for bounding boxes for ones of the plurality of visibleelements. In some embodiments, the coordinates for bounding boxes arehorizontal and vertical coordinates in a screen space for the graphicaluser interface. In other embodiments, three-dimensional bounding regionsmay be determined, or non-bounding coordinates may be utilized (e.g., aparticular point on each element such as the upper left-hand corner).

At 830, in the illustrated embodiment, the computing device determinesdepths within the hierarchical user interface code for the plurality ofvisible elements.

At 840, in the illustrated embodiment, the computing device generatesgroupings for the plurality of visible elements using unsupervisedclustering of the determined coordinates and depths. As discussed infurther detail below, in some embodiments, based on the groupings, thecomputing device classifies the plurality of visible elements accordingto known metadata values for the plurality of metadata fields andinformation indicating relationships between ones of the known metadatavalues and a plurality of types of visible user interface elements. Insome embodiments, the unsupervised clustering includes performing ak-means clustering process.

At 850, in the illustrated embodiment, the computing device storesinformation specifying the generated groupings.

In some embodiments, the computing device modifies depth values for onesof the plurality of elements to alter effects of differences in thedepth values, relative to effects of differences in the coordinates, inthe unsupervised clustering. In some embodiments, modifying the depthvalues includes applying an exponential function to the depth values. Insome embodiments, the computing device determines a similarity metricfor first and second groups of visible elements of the graphical userinterface.

Example Element Classification Techniques

FIGS. 9A and 9B are diagrams illustrating example techniques forclassifying elements within an identified component, according to someembodiments. In FIG. 9A, classification module 320 receives identifiedcomponents from clustering module 310. In the illustrated embodiment,classification module 320 classifies the elements within the identifiedcomponents and outputs classification information.

At element 912, in the illustrated embodiment, classification module 320retrieves element metadata from the DOM of one or more existing userinterfaces.

At element 914, in the illustrated embodiment, classification module 320accesses known metadata values for metadata fields and indications ofrelationships between the known metadata values and types of visibleuser interface elements. Examples of metadata fields for user interfaceelements (values for the metadata fields) are discussed below withreference to FIG. 10.

At element 916, in the illustrated embodiment, classification module 320scores element types based on values of the metadata fields and therelationship between the metadata values and one or more element types.Various scoring values are discussed below with reference to FIG. 10.

At element 918, in the illustrated embodiment, module 320 classifies theelement type based on the scoring (e.g., based on the highest score of aspecific user interface type for the element). In the illustratedembodiment, module 320 outputs classification information. Theclassification information generated by module 320 may be used astraining data for a machine learning system.

FIG. 10 is a table illustrating example scoring of a visible userinterface element for classification based on a plurality of metadatafield values, according to some embodiments. In the illustratedembodiment, classification module 320 determines metadata values 1030for each metadata field 1020 of a visible user interface element 1010.For example, in the illustrated embodiment, “span” is the metadata valuefor the tag metadata field. In the illustrated embodiment, based on thevalue 1030 obtained in each field 1020, module 320 awards points tocertain element types.

In the illustrated embodiment, three example element types are shown:label 1040, text 1050, and image 1060. In the illustrated embodiment,points are awarded to these three example element types based on values1030 and indications of relationships between values 1030 and variouselement types. In some embodiments, classification module 320 storesindications of relationships between metadata values and various elementtypes.

In the illustrated embodiment, a total 1070 is determined based on thetotal number of points awarded to each example element type. In someembodiments, classification module 320 assigns the highest scoringelement type to visible user interface element 1010. In the illustratedembodiment, label 1040 is the highest scoring element type with a totalscore of 4. Therefore, element 1010 is classified as a label elementtype in this example.

In some embodiments, a confidence value is determined for the assignedelement type based on the scoring of various other element types for agiven visible UI element. For example, in the illustrated embodiment,label 1040 received a total score of 4, while text 1050 and image 1060received total scores of 2 and 0, respectively. In some embodiments, theconfidence value for label 1040 is the total score for the label dividedby the sum of the scores of all of the element types (label, text, andimage in this example). Therefore, in this example, the confidence valuefor label 1040 is 0.666 or 66.6% (4 divided by 6). In other embodiments,other computations may be performed to generate a confidence value.

In some embodiments, the confidence values may be used as an input whendetermining component types. For example, if multiple components havebeen matched to a particular component type and a new component alsomatches the particular component type except for one element that had alow classification confidence, this may indicate that a new componenttype should not be added for the new component.

Metadata fields 1020 may contain HTML tags and attributes. For example,an HTML attribute may be one or more of the following: a class A, aclass B, a dir, etc. (such as those shown in the illustratedembodiment). Similarly, metadata values 1030 may be HTML attributevalues. For example, an HTML attribute value may be one or more of thefollowing: span, label, bBody, ltr, etc. (such as those shown in theillustrated embodiment). In other embodiments, other non-HTML types ofmetadata fields may be analyzed.

FIG. 11 is a block diagram illustrating an example method forclassifying visible user interface elements based on a plurality ofmetadata fields, according to some embodiments. The method shown in FIG.11 may be used in conjunction with any of the computer circuitry,systems, devices, elements, or components disclosed herein, among otherdevices. In various embodiments, some of the method elements shown maybe performed concurrently, in a different order than shown, or may beomitted. Additional method elements may also be performed as desired.

At 1110, in the illustrated embodiment, a computing device storesinformation specifying known metadata values for a plurality of metadatafields and indications of relationships between ones of the knownmetadata values and a plurality of types of visible user interfaceelements.

At 1120, in the illustrated embodiment, the computing device determinesrespective metadata values for a plurality of visible elements of agraphical user interface, wherein the metadata values are included inuser interface code that specifies the plurality of visible elements.

At 1130, in the illustrated embodiment, the computing device scores onesof the plurality of visible elements, based on the stored indications ofrelationships and the determined metadata values, to generate scorevalues for each of the plurality of types of visible elements. In someembodiments, the scoring is performed separately for different ones ofthe metadata fields, e.g., as shown in FIG. 10. In these embodiments,each metadata field may receive its own score(s) (e.g., a score for eachelement type). In some embodiments, the scoring is performed separatelyfor different ones of the plurality of types of user interface elements,e.g., as shown in FIG. 10. In these embodiments, each type may receiveone or more of its own scores (e.g., a score for each metadata value).In some embodiments, the types of visible elements include one or moreof the following: text, image, or link. In some embodiments, themetadata fields are specified for one or more metadata field types thatinclude: a field that specifies a placement of one or more elements in auser interface, a field that specifies one or more class names for anelement, a field that specifies a direction of text content of anelement, a field that indicates whether content of an element iseditable, a field that indicates whether an element can be dragged, afield that specifies how dragged data behaves when it is dropped, or afield that specifies a language of the content of an element.

At 1140, in the illustrated embodiment, the computing device classifies,based on the scoring, the plurality of visible elements according to theplurality of types of visible elements.

At 1150, in the illustrated embodiment, the computing device storesinformation specifying the classified elements. In some embodiments, thecomputing device uses the stored information as training data for one ormore machine learning modules configured to automatically generate oneor more interfaces. In some embodiments, the computing device uses theclassifications as inputs to a module that generates component typesbased on the classifications.

In some embodiments, the computing device determines whether a set ofelement types for a first group of elements is the same as a set ofelement types for a second group of elements. In some embodiments, thecomputing device determines whether a number of elements of each elementtype is the same for the first group of elements and the second group ofelements. This may facilitate component type identification, forexample, if the first and second group of elements correspond to thesame component type.

In some embodiments, the computing device determines whether an elementdisplayed at a position within the first group of elements is the sameelement type as an element displayed at a similar position within thesecond group of elements. In some embodiments, the computing devicedetermines whether the first group of elements and the second group ofelements have at least a threshold similarity, based on determiningwhether a set of element types for the first group of elements is thesame as a set of element types for the second group of elements thedetermining whether a number of elements of each element type is thesame for the first group of elements and the second group of elements.

In some embodiments, the computing device determines and stores aconfidence value associated with classification of one of the classifiedelements, where the determination is based on scores for multipledifferent types of elements generated for the one of the classifiedelements. In some embodiments, the confidence value is determined basedon a ratio of the score for a classified type for the classified elementto a sum of scores for all scored types for the classified element.

Example Component Type Identification Techniques

FIGS. 12A and 12B are diagrams illustrating example techniques foridentifying types of identified interface components, according to someembodiments. In FIG. 12A, component type identification module 330receives identified components (e.g., groups of nearby elements) fromclustering module 310. In the illustrated embodiment, component typeidentification module 330 uses classification information fromclassification module 320 to determine whether two components aresimilar. The techniques discussed below with reference to FIG. 12B areone non-limiting example of geospatial analysis for detecting componenttypes.

In some embodiments, classification module 320 is separate fromcomponent type identification module 330. In the illustrated embodiment,module 330 outputs component type definitions, based on a determinationof similarity between processed components.

At element 1210, in the illustrated embodiment, module 330 retrieveselement bounding information (e.g., element bounding boxes), from theDOM of one or more existing user interfaces, for the leaf elementscorresponding to visible nodes.

At element 1220, in the illustrated embodiment, module 330 determinestypes of elements within the groupings of elements, based onclassification information from module 320 and grouping information frommodule 310. Determining element types within components is discussedbelow with reference to FIG. 13.

At element 1222, in the illustrated embodiment, module 330 generatesgradients based on pixels of the element bounding boxes. In someembodiments, the gradient is based on the total number of pixels in thecomponent that the element is grouped in (e.g., the number of pixels inthe gradient for each element is relative to the total number of pixelsin the component). In some embodiments, the pixel gradient is generatedbased on the resolution of the component that the element belongs to.Example bounding box pixel gradients are discussed below with referenceto FIG. 15.

At element 1224, in the illustrated embodiment, module 330 generatesmatrices for each user interface component based on the pixel gradientsof the elements within the components. At element 1226, in theillustrated embodiment, module 330 implements a similarity functionusing transposed matrices to determine a similarity metric (e.g., asimilarity ratio). Matrix data structures for pixel gradients andimplementation of a similarity function are discussed below withreference to FIG. 16.

At element 1228, in the illustrated embodiment, module 330 determinesthat the two components are similar based on whether the determinedsimilarity metric is at or above a predetermined threshold. In theillustrated embodiment, at element 1228, module 330 outputs componenttype definitions for user interface components.

FIG. 13 illustrates example classification of two user interfacecomponents by a classification module within a component typeidentification module, according to some embodiments. In the illustratedembodiment, component type identification module 330 compares two userinterface components A and B 1332 with assistance from classificationmodule 320 to determine the composition of the two interface components.

In some embodiments, a match between the types of elements within twodifferent components (e.g., a match between the two compositions) isdetermined by classification module 320. In some embodiments, the matchis determined by the number of elements of each element type that arethe same for the two components. In addition, the position of theelements within the two components may be a factor in determining amatch between two components. For example, component A has an image,link, and label positioned at similar locations as the image, link, andlabel of component B. In this example, classification module 320 maydetermine that the compositions of component A and component B aresimilar and therefore, that the two compositions match.

In some embodiments, based on the compositions output by module 320,component type identification module 330 determines whether to calculatea similarity metric for the two components. Module 330 may allow somedifferences between the compositions of components 1332. For example, agiven component type may have an image and one to three text elements(such that different components of the same type can have differentnumbers of text elements). Similarly, a determined type of component maybe allowed an omission of one or more element types that are included inanother component of the same type.

Note that, in the illustrated example, the sizes of the link 1336 andlabel 1338 in the two components differ in width (e.g., the boundingboxes of the two elements are different sizes), which may affect thevalue of a similarity metric, but does not affect the composition ofelements in each component. In some embodiments, the classification isused as a filter. For example, further analysis may proceed to determinecomponent types only if each type of element in one component is alsorepresented in the other component. In some embodiments, analysis mayproceed only if each component includes the exact same number ofelements of each type. In other embodiments, the classifications may notbe used as a filter but may be another input to the determination of asimilarity metric.

FIGS. 14A and 14B illustrate example pixel characteristics forgenerating a pixel gradient, according to some embodiments. The pixelcharacteristics shown in the illustrated embodiment are used bycomponent type identification module 330 to determine a similaritymetric (e.g., a ratio) for components A and B 1332, once module 330 hasdetermined that the compositions of the two components match (e.g.,based on the output of module 320).

In FIG. 14A, a table is shown with two columns representing the pixelcolor 1410 and the pixel value 1420. In the left column, the pixel color1410 may be one of four options: white/empty, light gray, dark gray, orblack. In the right column, in the illustrated embodiment, a pixel valueis shown for each of the four pixel colors as 0, 0.33, 0.66, and 1,respectively.

The pixel colors may include various levels of granularity in thespectrum between white and black. For example, there may be twoadditional colors in the gradient from white to black, such as verylight gray and very dark gray with pixel values 0.165 and 0.83,respectively.

In FIG. 14B, a plot of pixel gradient values in relation to theirdistance from an adjacent pixel is shown. In the illustrated embodiment,the y-axis of the graph represents the pixel value 1420, while thex-axis represents the distance 1430 between adjacent pixels. In theillustrated example, the original pixel is a black pixel color 1410 witha pixel value of 1. Further, in the illustrated example, the pixelsadjacent to the original pixel decrease in value as the distance 1430between the original pixel and adjacent pixels increases. For example,the pixel located at 2 on the x-axis has a pixel value of 0.33 (e.g., alight gray pixel color).

Various techniques other than a pixel gradient may be used to process asimilarity metric for two components. For example, in some embodiments,a geometric process is used to determine a similarity metric for twocomponents based on the perimeter, total width, or length of boundingboxes of elements included in the two components. In another geospatialanalysis example, the total area inside element bounding boxes may beused to determine a similarity metric for two components.

FIG. 15 illustrates an example technique for generating pixel gradientsfor the bounding boxes of user interface elements within a component,according to some embodiments. In the illustrated embodiment, componenttype identification module 330 begins converting pixel values of elementbounding boxes into a pixel gradient. In the illustrated embodiment, avisual representation of a pixel line of an element bounding box isrepresented by bounding box portion 1520. In the illustrated embodiment,the original pixels of the bounding boxes are black with a pixel valueof 1 (shown as the black pixel line in the center of portion 1520). Inthe illustrated embodiment, the bounding boxes are converted to a pixelvalue representation displayed in table #1.

Once the bounding boxes of elements grouped within components areconverted to a pixel value representation, in the illustratedembodiment, a gradient is then applied to the pixel representation.Table #2, in the illustrated embodiment, displays the pixelrepresentation of the gradients.

In some embodiments, the number of pixels included in the gradient(e.g., the pixel values to the left and right of the original elementbounding box pixel in tables #1 and #2) is determined based on the totalsize and/or resolution of the component. For example, if the size and/orresolution of the component is larger the gradient may include a largenumber of pixels to the right and left of the original element boundingbox pixel, effectively causing the bounding box to appear relativelythinner.

FIG. 16 illustrates an example technique for determining a similaritymetric for two components based on pixel representations of boundingboxes, according to some embodiments. In the illustrated embodiment, thepixel gradients for a first component A are placed in a matrix A(g) 1640with width m and height n. In the illustrated embodiment, the matrixA(g) 1640 is flattened creating a matrix with width 1 and height n(matrix A(g, flat) 1610). A matrix for a second component B is alsogenerated and flattened to produce a flattened matrix 1620 with a singlecolumn.

As used herein, the term “flatten” refers to one or more matrixoperations that covert a matrix into a vector. For example, in disclosedembodiments, flattening a matrix refers to the operation of stacking oneor more columns of the matrix on top of each other to form a singlecolumn with multiple rows. Thus, in this stacking technique, the upper nentries of the flattened matrix correspond to the first column of theoriginal matrix, the next n entries correspond to the second column ofthe original matrix, and so on. For an M by N matrix, the correspondingflattened matrix has a size of M times N by 1. Flattening may also beperformed in row major order to form a single row with multiple columns.

In some embodiments, the matrix representations of the portions of thebounding boxes for two components are not the same size (e.g., theheight and/or width of the matrices are different). For example, one ofthe matrices may be smaller than the other. In some embodiments, thesmaller matrix is padded with zeroes in order to match the dimensions ofthe other matrix.

In the illustrated embodiment, a mathematical formula is implemented todetermine a similarity metric for the two components. In the illustratedembodiment, the dot product of matrix A(g, flat) and transposed matrixB(g, flat) is calculated, producing a match value between components Aand B 1332. In some embodiments, the match value represents a similaritybetween components A and B. In the illustrated embodiment, the dotproduct of matrix A(g, flat) and transposed matrix A(g, flat) iscalculated to produce a value associated with an exact match. The firstdot product is then divided by the second, to generate a similarityratio for components 1332, in the illustrated example.

In some embodiments, if the similarity ratio is above or equal to apredetermined threshold, the two components are considered similar. Notethat various different ratio values may be used for the predeterminedthreshold. For example, if predetermined threshold is 80%, thesimilarity ratio must be greater than or equal to 80% in order for twocomponents to be considered similar.

In some embodiments, based on the similarity ratio meeting thepredetermined threshold, module 330 assigns the same component type tothe two compared components. In some embodiments, this assignment iscalled the component type definition. In some embodiments, theassignment of the same component type to multiple components mayadvantageously prevent the production of duplicate components usingdifferent code. In addition, the assignment of a type to one or morecomponents may advantageously decrease search time for the identifiedcomponents in other existing user interfaces and thereby improvetraining data used to train a machine learning system. Note that, for aset of identified components, various techniques may be used to searchfor matches within the components to generate a corresponding set ofcomponent types. For example, each component may be compared with everyother component, or once a component type is identified, othercomponents may be compared with a single representative component of theidentified component type.

FIG. 17 illustrates an example method for identifying types of userinterface components, according to some embodiments. The method shown inFIG. 17 may be used in conjunction with any of the computer circuitry,systems, devices, elements, or components disclosed herein, among otherdevices. In various embodiments, some of the method elements shown maybe performed concurrently, in a different order than shown, or may beomitted. Additional method elements may also be performed as desired.

At 1710, in the illustrated embodiment, a computing device determines,based on user interface code, a plurality of visible elements of agraphical user interface.

At 1720, in the illustrated embodiment, the computing device determines,based on the user interface code, coordinates for bounding boxes forones of the plurality of visible elements.

At 1730, in the illustrated embodiment, the computing devices groups thevisible elements into at least first and second groups.

At 1740, in the illustrated embodiment, the computing device determinestypes of elements within the first and second groups.

At 1750, in the illustrated embodiment, in response to detecting a matchbetween the types of elements within the first and second groups, thecomputing device determines a similarity metric for the first and secondgroups based on the coordinates of determined bounding boxes within thefirst and second groups. In some embodiments, the computing devicedetects a match by determining whether a number of elements of eachelement type is the same for the first group of elements and the secondgroup of elements. In some embodiments, the computing device detects amatch by determining whether an element displayed at a position withinthe first group of elements is the same element type as an elementdisplayed at a similar position within the second group of elements.

In some embodiments, the similarity metric is determined based onrespective pixel representations for the first and second groups. Insome embodiments, the pixel representations are based on the determinedbounding boxes for ones of the plurality of visible elements. In someembodiments, the pixel representation is based on ones of the pluralityof visible elements of the graphical user interface.

In some embodiments, the pixel representations for the first and secondgroups are represented using respective matrix data structures. In someembodiments, the computing device flattens the matrix data structuresfor the first and second groups. In some embodiments, the computingdevice transposes the flattened matrix data structures for the first andsecond groups.

In some embodiments, the computing device determines a first dot productof the flattened matrix data structure for the first group and thetransposed matrix data structure for the second group. In someembodiments, the computing device determines a second dot product of theflattened matrix data structure for the first group and the transposedmatrix data structure for the second group. In some embodiments, thecomputing device calculates a result of the similarity metric based on aratio between the first dot product and the second dot product. In someembodiments, the first dot product corresponds to a similarity betweenthe first and second groups of elements and the second dot productcorresponds to a value associated with an exact match.

At 1760, in the illustrated embodiment, the computing device storesinformation defining a component type that corresponds to the first andsecond groups in response to the similarity metric meeting a thresholdvalue.

As discussed in detail below, identified component types may be used asbuilding blocks to automatically generate other user interfaces, in someembodiments.

Identifying Duplication Interface Design

In some embodiments, techniques similar to those discussed above fordetermining component types may be used to identify interface designthat is at least partially duplicated at different parts of aninterface. In some embodiments, this may include grouping elements intocomponents, and identifying similarity between components based on oneor more comparison techniques. Comparison techniques may includecomparing bounding boxes, comparing actual visual representations,comparing classified elements within components, etc.

Consider, for example, a first component with an image, a link with auser name, a title, and a label for a team member and a second componentwith an image, a link with text for a user name and record name, anevent, and a label for an event. The first component may be for JaneDoe, the VP of strategic events who is labeled as a manager. The secondcomponent may be for updating a record, performed by John Smith on acertain date. These two components may have substantially the samevisual and/or coding design, however, even though they are used todisplay different types of information, and this duplicate design may beautomatically identified and reported, in some embodiments.

In some embodiments, these techniques may advantageously identifyduplicate design, which may allow for automatic code merging or allow anadmin to make coding decisions based on the detected duplication. Insome embodiments, an administrator may design a component and submit itto a duplication detection engine, which may identify existingcomponents in an interface that meet a similarity threshold.

In some embodiments, for comparing bounding boxes and/or visualrepresentations, a geospatial database may be used. For example, thedatabase may store existing components and may be queried to detectsimilarities between stored components and a provided component. Onenon-limiting example of such a geospatial database include apostgres/postgis database. In other embodiments, non-database techniquesmay be used for geospatial analysis, e.g., such as the techniquesdiscussed with reference to FIGS. 15-16. In some embodiments, searchingfor duplicate components to generate a duplication report is performedusing a bi-directional long short-term memory (LSTM) recurrent neuralnetwork (RNN).

In some embodiments, classification module 320 may identify elementtypes within components being compared to generate a similarity value.Note that code regions identified by a duplicate design identificationmodule may or may not be similar enough to be considered the samecomponent type. For example, a 75% match may be considered sufficient toflag design duplication, but may not be sufficient to classify twocomponents as the same component type.

In some embodiments, both element classification and geospatial analysismay be used in combination to detect duplicate design.

The inputs to the duplicate design identification module may include acrawled web application then a DOM or sketch file, for example and themodule may output information identifying similar patterns in thecrawled web application. The disclosed techniques may be used bydevelopers to search for existing code blocks and/or designers to searchfor existing component designs and may reduce customization of redundantcomponents.

In some embodiments, based on a duplication report for existingcomponents, one or more components from the report are mapped to adatabased field. In some embodiments, the one or more mapped componentsmay advantageously allow an interface generator module to automaticallyselect one or more components from a database to generate a new userinterface. In some embodiments, generating a duplication report forsimilar existing user interface components may advantageously decreasedevelopment time for one or more future interfaces (e.g., developers areaware of which components/code blocks already exist).

FIG. 18 is a flow diagram illustrating an example method for identifyingduplicate interface design, according to some embodiments. The methodshown in FIG. 18 may be used in conjunction with any of the computercircuitry, systems, devices, elements, or components disclosed herein,among others. In various embodiments, some of the method elements shownmay be performed concurrently, in a different order than shown, or maybe omitted. Additional method elements may also be performed as desired.

At 1810, in the illustrated embodiment, a computing system determinesbased on hierarchical user interface code, a plurality of visibleelements of a graphical user interface. These may be leaf nodes in a DOMrepresentation, for example.

At 1820, in the illustrated embodiment, the computing system determinescoordinates within the interface for ones of the plurality of visibleelements. The coordinates may correspond to points associated with agiven element (e.g., the center or corner of an element) or other shapessuch as bounding boxes of visual representations of the element orpoints associated with such visual representations.

At 1830, in the illustrated embodiment, the computing system determinesdepth levels within the hierarchical user interface code for theplurality of visible elements. Speaking generally, elements at similardepths may be more likely to be grouped, in some embodiments.

At 1840, in the illustrated embodiment, the computing system groups setsof the plurality of visible elements based on the determined coordinatesand depth levels. In some embodiments, the system determines one or morecomponent types for the groups of elements. Groups corresponding to thesame component type may be identified as meeting a threshold similaritycriterion (although a different, e.g., higher threshold may be used foridentifying similarity and component types in other embodiments).

At 1840, in the illustrated embodiment, the computing system identifiestwo or more groups that meet a threshold similarity criterion. Thecomputing system may generate a report that identifies the groups. Insome embodiments, the computing system automatically merges code forgroups that meet the threshold criterion. For example, the system maygenerate a class or component type for the groups such that a developercan change the class or component type once to adjust multiplecorresponding code segments.

The identification may be based on classification of elements and/orgeospatial analysis.

Example Interface Generator Techniques

FIG. 19 is a block diagram illustrating an interface generator module,according to some embodiments. In the illustrated embodiment, module 130stores component types 1910 and display parameters 1920. Thisinformation may be automatically generated based on existing interfacesand/or user specified. For example, grouping module 120 may generatethis information and/or a user may generate or modify the information.Component types are discussed in detail above. Display parameter 1920,in some embodiments, indicate formatting within components and/or amongcomponents, and may indicate additional information such as fonts,colors, etc.

In the illustrated embodiment, module 130 includes a component selectionmodule 1930 and a formatting module 1940.

Component selection module 1930, in some embodiments, is configured toreceive input data for a user interface and automatically select one ormore component types to represent the user input data. The input datamay be in Javascript Object Notation (JSON) format, for example, or someother organization. The input data may not, however, specify how it isto be displayed. In some embodiments, component selection module 1930 isconfigured to determine element types in the input data and match thosetypes to element types in known component types 1910. Based on thismatching, component selection module 1930 may select one or morecomponent types.

Note that, for a given set of input data, component selection module1930 may select multiple component types as discussed below withreference to FIG. 20. In some embodiments, component selection module1930 may select one or more component types that provide the best matchfor the input data, even if the types of elements are not an exactmatch.

Formatting module 1940, in the illustrated embodiment, is configured toformat the components according to the selected component types anddisplay parameters 1920. In some embodiments, the formatting is based onadditional constraints, such as screen resolution, screen size, displaytechnology, specified fonts, etc. Module 1940 may layout selectedcomponents, format multiple components, layout elements within selectedcomponents, and/or format within selected components. In someembodiments, formatting module 1940 is configured to output userinterface data, and may cause display of an interface based on the userinterface data. Formatting may include positioning, color, font,spacing, layout, size, rotation, transparency, borders, etc. withinand/or among components. In some embodiments, the display parameters1920 may include information indicating positioning of certain componenttypes in prior interfaces, which may be used to position correspondingcomponents in automatically generated interfaces.

As discussed in further detail below, component types 1910, displayparameters 1920, module 1930, and/or module 1940 may be modified basedon user input and/or interface interaction. For example, module 130 maypresent component type and/or formatting options to an administrator,who may select desired options. In some embodiments, modules 1930 and/or1940 implement one or more machine learning engines that may be trainedto provide more desirable outputs based on administrator input.Similarly, information regarding user interaction with the interfacesuch as time spent on portions of the interface, selection activity(e.g., clicking), etc. may be used to train these modules (and/or othermodules discussed herein).

FIG. 20 illustrates two example component types selected for a set ofinput data 2010, according to some embodiments. In the illustratedembodiment, a set of input data 2010 for an article includes a link tothe article, two separate text elements (describing the article andproviding a date of the article), and an image from the article. Forexample, a component based on this data may be displayed on a front pageof a web interface and selectable to read the article. In theillustrated embodiment, module 130 may select two component types A andB 2020 and 2030 for this input data. As shown, the two component typeshave the same set of element types but are formatted quite differently.Module 130 may select a final component based on user input or based oncontrol by formatting module 1940, for example. In some embodiments, auser may enter a set of data to be displayed and search for matchingcomponent types. The system may display multiple potential componenttypes and allow the user to select a preferred type.

In some embodiments, generating user interfaces using automatictechniques may advantageously decrease maintenance and/or migration ofuser interfaces, thereby preventing a build-up of user interface codefrom previously existing user interfaces. Further, automaticallygenerating interfaces may be advantageous by allowing differences ininterfaces generated by the same interface module. For example, fordifferent languages with different average word lengths, module 130 maygenerate different interfaces for the same underlying information, tobetter display the information in different languages.

In some embodiments, the interface generator module 130 generates userinterface data based on voice interactions. For example, after receivinginstructions from a user through voice interactions, module 130 maygenerate a user interface that summarizes the interactions, therebyallowing the user to confirm or modify the interactions. In someembodiments, the user interface summary of the voice interactions mayadvantageously allow the user to provide accurate instructions whiledriving, walking, etc. with a small amount of interaction requiredthrough touch (e.g., screen clicking). In some embodiments, interfacegenerator module 130 generates a user interface for one or more of thefollowing display technologies: a graphical user interface, a voice userinterface (VUI), virtual and/or augmented reality (VR/AR), head-updisplay (HUD), etc. In some embodiments, the target output platform maybe an input constraint that is used in selecting component types. Forexample, some component types may be determined to be more desirable foraugmented reality than for a traditional display.

In some embodiments, the interface generator module 130 may provide aseamless experience across multiple types of output devices and mayautomatically select one or more output types to display certaincomponents automatically. For example, based on context parameters,module 130 may decide to output a first component via a monitor andaudibly, but output a second component via augmented reality only.

In some embodiments, the disclosed techniques may facilitate integrationof external services into an existing interface. For example, thedisclosed techniques may be used to automatically format interfaces withdata from the external database to maintain the look and feel of theexisting database.

FIG. 21 is a flow diagram illustrating an example component-based methodfor automatically generating a user interface, according to someembodiments. The method shown in FIG. 21 may be used in conjunction withany of the computer circuitry, systems, devices, elements, or componentsdisclosed herein, among others. In various embodiments, some of themethod elements shown may be performed concurrently, in a differentorder than shown, or may be omitted. Additional method elements may alsobe performed as desired.

At 2110, in the illustrated embodiment, a computing system storestemplate information that defines a plurality of component types and oneor more display parameters identified for one or more user interfaces.In the illustrated embodiment, a component type specifies a set of oneor more user interface element types included in the component and adisplay parameter specifies how one or more components are to bedisplayed. In some embodiments, a plurality of component types aredetermined from one or more existing user interfaces, according to thetechniques discussed above. In some embodiments, the computing systemimplements style transfer functionality to transfer the style of one ofthe existing interfaces to the automatically generated interface and/ormerge two or more styles. Style may be implemented by selectingcorresponding component types, display parameters, fonts, etc.

At 2120, in the illustrated embodiment, the computing system receives arequest to automatically generate a user interface in accordance withthe template information, wherein the request specifies data for a setof elements to be displayed in the user interface.

At 2130, in the illustrated embodiment, the computing system groups onesof the set of elements into multiple components according to multipleones of the component types. In some embodiments, a machine learningmodule groups elements into components and may be trained based on pastuser input and/or user interactions with automatically generatedinterfaces.

At 2140, in the illustrated embodiment, the computing system formats thecomponents according to the display parameters. The display parametersmay specify formatting within one or more component types and/or amongmultiple components. The formatting may be based on one or more displaycharacteristics of a display device. In non-visual interfaceembodiments, the formatting may be based on one or more output devicecharacteristics (e.g., audio device type, target environment, etc.).

At 2150, in the illustrated embodiment, the computing system causesdisplay of a user interface that displays the components based on theformatting. This may include transmitting the interface via one or morecommunication channels or controlling a display device, for example.

Example Style Transfer Techniques

As briefly discussed above, the disclosed techniques may be used toperform a style copy procedure. For example, the existing interface maybe crawled and analyzed to determine a plurality of component typesand/or display parameters. Examples of display parameters include,without limitation, layout parameters, formatting parameters, fonts,etc.

This component types and display information may then be used togenerate an interface that exhibits the same style as the crawledinterface. The new interface may be specified using input informationthat does not specify formatting, or using code such as a DOM that ismodified based on the component types from the crawled interface. Insome embodiments, component types may be determined based on elementsfrom both interfaces (e.g., to generate new component types that do notexist in either interface. In some embodiments, interfaces may be mergedto maintain component types from both interfaces in an output interface.

This may allow an entity to use an out-of-the-box template to initiallyset up their web application, but then copy style from anotherinterface, such as their marketing site to achieve the correct style.

In some embodiments, an analysis module may also provide informationregarding conventionality and/or design heuristics of the targetinterface. This module may provide insight regarding how similar thetarget is to known components, for example. The module may provide atopology score, which may be implemented as a chrome extension, sketchplugin, or app builder to facilitate manual interface design. In someembodiments, this module may objectively score designs based on thequality of their components, the number of different component typesused, etc.

Example Display Device Constraints

As briefly discussed above, interface generator module 130 may generateinterfaces based on one or more input constraints. For example, certainlayout techniques may be preferable for smaller or larger displays orcertain resolutions. Further, certain component types may be preferablefor various device constraints. In some embodiments, interface generatormodule 130 may select from among multiple matching component types for aset of input data based on one or more device constraints such asresolution, display type, or display size. In some embodiments,interface generator module 130 may use different formatting and/orlayout techniques based on different values of constraints. Therefore,the same underlying interface code may automatically generate markedlydifferent interfaces in different scenarios, in some embodiments,without requiring hard-coding of templates for the different scenarios.Additional examples of constraints include user profile information,location, record type, domain, community, etc. In some embodiments,machine learning models may be separately trained for differentconstraint values.

Automatically Generating Interfaces for Predicted Events

In some embodiments, a computing system (which may or may not be thesame computing system that implements interface generator module 130) isconfigured to predict that user input will be needed and automaticallyinitiate a procedure that requests user input, e.g., based on one ormore automation parameters. As one example, based on a user crossing ageo-fence, the computing system may initiate a procedure to request userinput to book a hotel. As another example, based on a change in acustomer relationship management database, the computing system mayinitiate a procedure to update a follow-up date for an event. In someembodiments, there is no pre-determined interface template for theinitiated procedure. Rather, in these embodiments, interface generatormodule 130 is configured to automatically select one or more componenttypes for an interface to request entry of the user input. Module 130may also format within and/or among selected components. Note that theprediction may be speculative in nature, e.g., the user may decide notto provide input.

Automation parameters may include, for example: threshold velocity,location, calendar information, account information, locationinformation, time information, information regarding communications,etc. In various embodiments, these parameters may be used to determinewhen to speculatively initiate a procedure. In some embodiments, some ofthese parameters may also be used as context parameters to constrain theinterface that is automatically generated.

Note that interface generator module 130 may also select from amongmultiple types of interfaces to output the speculatively-generatedinterface. For example, based on a current velocity of the user, thesystem may predict that the user is driving and determine that a voiceinterface may be most effective. In this scenario, the computing devicemay call the user via a phone (or output audio directly if the device isa mobile device or vehicle computer system). In other situations, thecomputing system may determine to visually display the interface. Notethat the computing system may select a channel for delivering thegenerated user interface based on context information as well, e.g.,determining whether to send an SMS message, initiate a phone call, use aBluetooth connection, use an internet connection, etc. For various typesof interfaces, the selected component types may be used to controloutput of the interface information and receive user input. For example,audio output may be ordered based on the selected component type or avisual output may be formatted based on the selected component type.

In some embodiments, a prediction module is configured to generate a setof input data (e.g., in a JSON format) and provide it to interfacegenerator module 130 without indicating formatting or organization ofthe input data. Module 130 may automatically generate an interface forthe predicted task based on the various techniques discussed above. Theinterface generator module 130 may include an artificial intelligenceengine to improve component type selection and formatting based on userinteraction with generated interfaces for predicted tasks and/oradministrator feedback.

In some embodiments, module 130 may automatically generate componenttypes and/or display parameters from an existing user interface usingany of the various techniques disclosed herein in order to match thelook and feel of the existing interface when automatically generatinginterfaces for predicted events. This may allow the computing system tospeculatively generate such interfaces even when it does not control theexisting interface, for example.

In some embodiments, user information in the form of behavior, patterns,context, and/or signals are used to predict what users will likely bedoing in the future on their device (e.g., changes to be displayed on ascreen of a device of the user). In some embodiments, user informationincludes one or more of the following: calendar, email, location, time,velocity, etc. In some embodiments, user activity, such as clickingand/or usage patterns is used to predict user intentions (e.g., what theuser desires to do at a given point in time).

In some embodiments, user information used to predict and generate oneor more user interfaces for a user may advantageously improve navigationwithin the user interface (e.g., the user can locate what they arelooking for quickly).

FIG. 22 is a flow diagram illustrating a method for automaticallyselecting component types for a predictive interface, according to someembodiments. The method shown in FIG. 22 may be used in conjunction withany of the computer circuitry, systems, devices, elements, or componentsdisclosed herein, among others. In various embodiments, some of themethod elements shown may be performed concurrently, in a differentorder than shown, or may be omitted. Additional method elements may alsobe performed as desired.

At 2210, in the illustrated embodiment, a computing system automaticallyinitiates a procedure that requests user input based on one or moreautomation parameters.

At 2220, in the illustrated embodiment, the computing system determinesa set of user interface elements for an interface to request entry ofthe user input. This may be based on input data that specifiesinformation to be displayed and information to be received.

At 2230, in the illustrated embodiment, the computing system selects oneor more component types that include types of user interface elementsfor the determined set of user interface elements. Note that a directmatch of element types may not be required, but module 130, for example,may attempt to select a component that includes fields for all the inputdata. In some embodiments, component types may be selected based on oneor more context parameters. In some embodiments, component types may beselected based on a target interface type (e.g., based on displayresolution, type of display, whether the display includes audio and/orvisual outputs, etc.).

At 2240, in the illustrated embodiment, the computing system causesoutput of one or more components with the user interface elementsaccording to the selected ones or more component types. In someembodiments, this may include sending information specifying the userinterface via one or more communication channels. Note that variouselements discussed above may be performed locally by the device thatoutputs the components or by another device (e.g., by a remote server).

Example Machine Learning Techniques

As noted above, interface generator module 130 may, in variousembodiments, use one or more machine learning modules to automaticallygenerate user interface data. Further, as discussed above, variousinformation may be used as training or input data for these machinelearning modules, such as component type definitions, formattinginformation, display parameters, grouping information, classificationinformation, etc. As described in more detail below with reference toFIGS. 23-25, various embodiments may utilize user feedback as trainingdata to further refine one or more machine learning modules used toautomatically generate user interfaces.

Referring now to FIG. 23, a block diagram of an example embodiment ofinterface generator module 130 is depicted, according to someembodiments. In FIG. 23, interface generator module 130 includescomponent selection module 1930, formatting module 1940, and machinelearning modules 2304. Note that, although shown separately in FIG. 23,one or more machine learning modules 2304 may be included as part ofcomponent selection module 1930 or formatting module 1940, in someembodiments. Machine learning modules 2304, also referred to herein asmachine learning engines, may use any of various suitable machinelearning algorithms. For example, in some embodiments, the machinelearning engines may use neural networks, vector machines, gradientboosting, naïve Bayes, linear regression, logistic regression,reduction, random forest, etc.

FIG. 23 further includes template information 2302. In variousembodiments, the template information 2302 defines a plurality ofcomponent types 1910 and one or more display parameters 1920 identifiedfor one or more user interfaces. As discussed in more detail above, acomponent type, in various embodiments, specifies a set of one or moreuser interface element types included in a given component, and adisplay parameter specifies how one or more components are to bedisplayed (e.g., within a larger user interface). Note that, in variousembodiments, template information 2302 (including component types 1910and display parameters 1920) may be either stored by module 130 or onone or more storage media accessible to module 130.

As described herein, interface generator module 130 is operable toautomatically generate user interface data, according to variousembodiments. For example, in the depicted embodiment, interfacegenerator module 130 receives a request to generate a user interface,where the request includes an input data set 2310. As described above inreference to FIG. 19, interface generator module 130 may generate userinterface data 2312 by selecting one or more component types, elementtypes, and display parameters used to display the input data set 2310.The user interface data 2312, in various embodiments, may specify one ormore user interfaces that may be provided to end users. Further, in someembodiments, the user interface data 2312 may specify multiple suggestedcomponents, component types, or user interfaces that may be provided toa user (e.g., an administrator or UX designer) who may select one ormore aspects of the user interface that is ultimately exposed to endusers, as described in reference to FIG. 24.

Interface generator module 130 may receive user feedback 2314 from oneor more users based on the user interface data 2312. In someembodiments, the user feedback 2314 may correspond to user interactionwith a user interface, including interactions such as selection activity(e.g., clicking), the time spent viewing different portions of thecomponent or interface, user interface elements over which the userhovered a cursor, etc. In some embodiments, the user feedback 2314 maycorrespond to the user interaction of individual users or to the userinteraction of one or more groups of users.

In embodiments in which multiple suggested user interfaces or userinterface components are provided to a user, the user feedback 2314 mayinclude information about the suggested interface or component selectedby the user. For example, the user feedback 2314 may indicate which ofthe suggested user interfaces or user interface components was selected,specify a selected component type, specify various display parameters orformatting characteristics associated with the selected user interfaceor component(s) (e.g., size, rotation, layout, font color, font size,etc.), etc.

In various embodiments, the user feedback 2314 may be used to train oneor more of the machine learning modules 2304. For example, in someembodiments, one or more of the machine learning modules 2304 may usedata corresponding to user interaction with a given user interface orcomponent as training data in an attempt to maximize or encourageparticular user behavior, such as increasing the number of times linkelements are selected, increasing the amount of time spent viewingvarious portions of the interface, etc. Further, in various embodiments,machine learning modules 2304 may be trained based on user feedback 2314associated with a selected one of multiple suggested user interfaces orcomponents for an input data set 2310. For example, one or more machinelearning modules 2304 may use, as training data, the information (e.g.,display parameters, component types, etc.) associated with the componentor user interface selected by a user (e.g., a designer). Based on thisdata, the machine learning modules may modify the manner in which thesuggested user interfaces or components are selected, in someembodiments. For example, in some embodiments, after undergoing trainingbased on this feedback data, interface generator module 130 may suggestuser interfaces or components that are similar to those previouslyselected by the user, similar users, or for similar input data sets2310. Similarly, interface generator module 130 may format withincomponents or among components based on the feedback data. Note thatmachine learning modules 2304 may be trained based on the user feedback2314 in a supervised or unsupervised manner, according to variousembodiments.

In some embodiments, multiple types of machine learning modules may beused to automatically generate user interfaces. For example, one machinelearning engine may select component types and another engine may layoutand format selected components. In embodiments with refinement ofmachine learning modules based on user feedback, all or a portion of theimplemented types of machine learning modules may be refined.

FIG. 23 further depicts profile data 2306. As noted above, in someembodiments, the user feedback 2314 may correspond to the userinteraction of individual users or to the user interaction of one ormore groups of users. In some such embodiments, data corresponding toindividual users or groups of users may be used to generate profiles,specified in profile data 2306, for those individual users or groups,which, in turn, may be used as an input to one or more of the machinelearning modules 2304. In various embodiments, this may enable interfacegenerator module 130 to automatically generate user interfaces or userinterface components that are customized for a given individual or groupbased on the individual or group's respective user activity.

FIG. 24 illustrates an example interface used to suggest variousautomatically generated user interface components, according to someembodiments. In FIG. 24, interface 2400 depicts components 2402-2406,each of which may be suggested user interface components used to displaya portion of the input data set 2310. For example, as discussed withreference to FIG. 23, interface generator module 130 may receive arequest to generate a user interface, where the request specifies aninput data set 2310. Interface generator module 130 may thenautomatically select (e.g., using component selection module 1930)various suggested component types to use to display the input data set2310. Further, in various embodiments, module 130 may automaticallygenerate representative components (e.g., components 2402-2406)corresponding to the suggested component types, and may cause display ofthe interface 2400 that includes the representative components 2402-2406to a user. The user may then select one or more of the suggestedcomponents 2402-2406 to use to display the input data set 2310. Thisselection may be used to automatically generate, for the input data set2310, a user interface to expose to one or more end users. Further, asdiscussed above, the user's selection may be provided as user feedback2314 to interface generator module 130 to be used as training data forone or more machine learning module 2304.

Note that, in various embodiments, the accuracy of the components2402-2406 suggested to the user may be improved through an iterativereinforcement learning process in which the module 130 is furtherrefined based on the user feedback 2314. That is, applying variousdisclosed techniques, interface generator module 130 may be optimized toselect the correct components to represent a given input data set 2310and lay the suggested components out, within a user interface, followingbest practices derived through the machine learning model. Thus, invarious embodiments, interface generator module 130 may use the userselections to refine machine learning module(s) 2304 and suggestimproved user interfaces to the user.

Further note that, for clarity, only three components are shown ininterface 2400. This embodiment is provided merely as an example and isnot intended to limit the scope of the present disclosure. In otherembodiments, any suitable number of components may be provided ininterface 2400.

Referring now to FIG. 25, a flow diagram illustrating an example method2500 for refining, based on user feedback, machine learning engines usedto automatically generate component-based user interfaces, according tosome embodiments. The method shown in FIG. 25 may be used in conjunctionwith any of the computer circuitry, systems, devices, elements, orcomponents disclosed herein, among others. In various embodiments,method 2500 may be performed to refine one or more machine learningmodules 2304 included in the interface generator module 130 of FIG. 23.In various embodiments, method 2500 may be performed by a computersystem that includes (or has access to) a non-transitory,computer-readable medium having program instructions stored thereon thatare executable by the computer system to cause the operations describedwith reference to FIG. 25. In FIG. 25, method 2500 includes elements2502-2512. While these elements are shown in a particular order for easeof understanding, other orders may be used. In various embodiments, someof the method elements may be performed concurrently, in a differentorder than shown, or may be omitted. Additional method elements may alsobe performed as desired.

At 2502, in the illustrated embodiment, a computer system storestemplate information that defines a plurality of component types and oneor more display parameters identified for one or more user interfaces.In the illustrated embodiment, a component type specifies a set of oneor more user interface element types included in a given component, anda display parameter specifies how one or more components are to bedisplayed.

At 2504, in the illustrated embodiment, the computer system receives arequest to automatically generate a user interface, where the requestspecifies a data set to be displayed using the user interface.

At 2506, in the illustrated embodiment, the computer systemautomatically generates at least one user interface in response to therequest, where the generating is performed by one or more machinelearning modules that use the template information and the data set asinputs. For example, in response to the request, one or more machinelearning modules 2304 may use the input data set 2310 and the templateinformation 2302 to generate user interface data 2312. As noted above,user interface data 2312 may specify one or more automatically generateduser interfaces.

At 2508, in the illustrated embodiment, the computer system provides theat least one user interface to one or more users. In some embodiments,provided interfaces may include a selection of one or more componenttypes through which to display the input data set 2310 and anarrangement of one or more components, corresponding to the one or morecomponent types, displaying the input data set 2310 in one or more userinterface elements.

At 2510, in the illustrated embodiment, the computer system receivesuser feedback associated with the at least one user interface. As notedabove, the user feedback 2314 may take various forms. For example, insome embodiments, the user feedback includes data corresponding to userinteraction with the at least one user interface. In some embodiments,for example, the data corresponding to the user interaction includes atleast one of click data for one or more user interface elements includedin the at least one user interface, or hover data for one or more userinterface elements included in the at least one user interface. In otherembodiments, the user feedback 2314 may include data corresponding tothe user interaction of a particular user or a particular set of one ormore users (e.g., all or a subset of users associated with a particulartenant in a multi-tenant system).

At 2512, in the illustrated embodiment, method 2500 includes training atleast one of the one or more machine learning modules based on the userfeedback. In some embodiments, element 2512 may include selectivelytraining at least one of the one or more machine learning modules basedon the data corresponding to the user interaction of a particular useror a particular set of one or more users.

Note that, in some embodiments, automatically generating the at leastone user interface may include automatically selecting a plurality ofsuggested component types for a subset of the input data set 2310. Forexample, in some such embodiments, method 2500 may further includeautomatically generating, for the plurality of suggested componenttypes, a corresponding plurality of representative components, andcausing display of a particular user interface that includes thecorresponding plurality of representative components, where each of thecorresponding plurality of representative components depicts the subsetof the data set. Further, in some such embodiments, the computer systemmay receive input indicating a selected component type of the pluralityof suggested component types, where the at least one user interfaceprovided to the one or more users includes at least a portion of thedata set depicted using the selected component type.

Further note that, in some embodiments, providing the at least one userinterface to the one or more users includes providing the at least oneuser interface in a first interactive format (e.g., via display on acomputer monitor) and in a second, different interactive format (e.g.,via an AR/VR device, HUD device, display on mobile device, an audioformat, or any other suitable interactive format), and the user feedback2314 may include data specifying an interactive format used by the oneor more users to interact with the at least one user interface.

In some embodiments, method 2500 may further include modifying thetemplate information 2302 based on the user feedback 2314. For example,in some embodiments, the computer system may modify data associated withthe component types 1910 or display parameters 1920 based on theparticular component types or display parameters associated with aselected component type chosen by a user (e.g., an administrator).Additionally, in embodiments in which the user feedback 2314 includesdata corresponding to the user interaction of a particular user or aparticular set of one or more users, method 2500 may include generatinga profile for the particular user, or the particular set of one or moreusers, where the profile may then be used as an input for at least oneof the one or more machine learning modules 2304.

Example Computing Device

Turning now to FIG. 26, a block diagram of a computing device (which mayalso be referred to as a computing system) 2610 is depicted, accordingto some embodiments. Computing device 2610 may be used to implementvarious portions of this disclosure. Computing device 2610 is oneexample of a device that may be used as a mobile device, a servercomputer system, a client computer system, or any other computing systemimplementing portions of this disclosure.

Computing device 2610 may be any suitable type of device, including, butnot limited to, a personal computer system, desktop computer, laptop ornotebook computer, mobile phone, mainframe computer system, web server,workstation, or network computer. As shown, computing device 2610includes processing unit 2650, storage subsystem 2612, and input/output(I/O) interface 2630 coupled via interconnect 2660 (e.g., a system bus).I/O interface 630 may be coupled to one or more I/O devices 2640.Computing device 2610 further includes network interface 2632, which maybe coupled to network 2620 for communications with, for example, othercomputing devices.

Processing unit 2650 includes one or more processors, and in someembodiments, includes one or more coprocessor units. In someembodiments, multiple instances of processing unit 2650 may be coupledto interconnect 2660. Processing unit 2650 (or each processor withinprocessing unit 2650) may contain a cache or other form of on-boardmemory. In some embodiments, processing unit 2650 may be implemented asa general-purpose processing unit, and in other embodiments it may beimplemented as a special purpose processing unit (e.g., an ASIC). Ingeneral, computing device 2610 is not limited to any particular type ofprocessing unit or processor subsystem.

As used herein, the terms “processing unit” or “processing element”refer to circuitry configured to perform operations or to a memoryhaving program instructions stored therein that are executable by one ormore processors to perform operations. Accordingly, a processing unitmay be implemented as a hardware circuit implemented in a variety ofways. The hardware circuit may include, for example, customvery-large-scale integration (VLSI) circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A processing unit may also be implemented inprogrammable hardware devices such as field programmable gate arrays,programmable array logic, programmable logic devices, or the like. Aprocessing unit may also be configured to execute program instructionsor computer instructions from any suitable form of non-transitorycomputer-readable media to perform specified operations.

Storage subsystem 2612 is usable by processing unit 2650 (e.g., to storeinstructions executable by and data used by processing unit 2650).Storage subsystem 2612 may be implemented by any suitable type ofphysical memory media, including hard disk storage, floppy disk storage,removable disk storage, flash memory, random access memory (RAM-SRAM,EDO RAM, SDRAM, DDR SDRAM, RDRAM, etc.), ROM (PROM, EEPROM, etc.), andso on. Storage subsystem 2612 may consist solely of volatile memory insome embodiments. Storage subsystem 2612 may store program instructionsexecutable by computing device 2610 using processing unit 2650,including program instructions executable to cause computing device 2610to implement the various techniques disclosed herein.

I/O interface 2630 may represent one or more interfaces and may be anyof various types of interfaces configured to couple to and communicatewith other devices, according to various embodiments. In someembodiments, I/O interface 2630 is a bridge chip from a front-side toone or more back-side buses. I/O interface 2630 may be coupled to one ormore I/O devices 2640 via one or more corresponding buses or otherinterfaces. Examples of I/O devices include storage devices (hard disk,optical drive, removable flash drive, storage array, SAN, or anassociated controller), network interface devices, user interfacedevices or other devices (e.g., graphics, sound, etc.).

It is noted that the computing device of FIG. 26 is one embodiment fordemonstrating disclosed concepts. In other embodiments, various aspectsof the computing device may be different. For example, in someembodiments, additional components, or multiple instances of theillustrated components may be included.

Although specific embodiments have been described above, theseembodiments are not intended to limit the scope of the presentdisclosure, even where only a single embodiment is described withrespect to a particular feature. Examples of features provided in thedisclosure are intended to be illustrative rather than restrictiveunless stated otherwise. The above description is intended to cover suchalternatives, modifications, and equivalents as would be apparent to aperson skilled in the art having the benefit of this disclosure.

The scope of the present disclosure includes any feature or combinationof features disclosed herein (either explicitly or implicitly), or anygeneralization thereof, whether or not it mitigates any or all of theproblems addressed herein. Accordingly, new claims may be formulatedduring prosecution of this application (or an application claimingpriority thereto) to any such combination of features. In particular,with reference to the appended claims, features from dependent claimsmay be combined with those of the independent claims and features fromrespective independent claims may be combined in any appropriate mannerand not merely in the specific combinations enumerated in the appendedclaims.

1. A method, comprising: storing, by a computing system, templateinformation that defines a plurality of component types and one or moredisplay parameters identified for one or more user interfaces, wherein acomponent type specifies a set of one or more user interface elementtypes included in a given component, and wherein a display parameterspecifies how one or more components are to be displayed; receiving, bythe computing system, a request to automatically generate a userinterface, wherein the request specifies a data set to be displayedusing the user interface; automatically generating, by the computersystem, at least one user interface in response to the request, whereinthe generating is performed by one or more machine learning engines thatuse the template information and the data set as inputs; providing, bythe computer system, the at least one user interface to one or moreusers; receiving, by the computer system, user feedback associated withthe at least one user interface, wherein the user feedback includes datacorresponding to user interaction with the at least one user interface;and training at least one of the one or more machine learning enginesbased on the data corresponding to the user interaction with the atleast one user interface.
 2. The method of claim 1, wherein the at leastone user interface includes: a selection of one or more component typesthrough which to display the data set; and an arrangement of one or morecomponents, corresponding to the one or more component types, displayingthe data set in one or more user interface elements.
 3. The method ofclaim 1, wherein the automatically generating the at least one userinterface comprises: automatically selecting a plurality of suggestedcomponent types for a subset of the data set; wherein the user feedbackindicates one of the plurality of suggested component types.
 4. Themethod of claim 3, further comprising: automatically generating, for theplurality of suggested component types, a corresponding plurality ofrepresentative components; causing display of a particular userinterface that includes the corresponding plurality of representativecomponents, wherein each of the corresponding plurality ofrepresentative components depicts the subset of the data set; andreceiving, by the computer system, input indicating a selected componenttype of the plurality of suggested component types, wherein the at leastone user interface, provided to the one or more users, includes at leasta portion of the data set depicted using the selected component type. 5.(canceled)
 6. The method of claim 1, wherein the data corresponding tothe user interaction includes at least one of click data for one or moreuser interface element included in the at least one user interface, orhover data for one or more user interface element included in the atleast one user interface.
 7. The method of claim 1, wherein the userfeedback includes data corresponding to user interaction, with the atleast one user interface, of a particular user, wherein the methodfurther comprises: based on the data corresponding to the userinteraction of the particular user, generating a profile for theparticular user, wherein the profile is used as an input for at leastone of the one or more machine learning engines.
 8. The method of claim1, wherein the user feedback includes data corresponding to userinteraction, with the at least one user interface, of a particular setof one or more users, wherein the method further comprises: selectivelytraining at least one of the one or more machine learning engines basedon the data corresponding to the user interaction of the particular setof one or more users.
 9. The method of claim 1, wherein the providingthe at least one user interface to the one or more users comprises:providing, to the one or more users, the at least one user interface ina first interactive format; and providing, to the one or more users, theat least one user interface in a second, different interactive format;wherein the user feedback includes data specifying an interactive formatused by the one or more users to interact with the at least one userinterface.
 10. The method of claim 1, further comprising: modifying, bythe computer system, the template information based on the userfeedback.
 11. A non-transitory computer-readable medium havinginstructions stored thereon that are capable of execution by a computingdevice to perform operations comprising: storing template informationthat defines a plurality of component types and one or more displayparameters identified for one or more user interfaces, wherein acomponent type specifies a set of one or more user interface elementtypes included in a given component, and wherein a display parameterspecifies how one or more components are to be displayed; receiving arequest to automatically generate a user interface, wherein the requestspecifies a data set to be displayed using the user interface;automatically generating at least one user interface in response to therequest, wherein the generating is performed by one or more machinelearning engines that use the template information and the data set asinputs; providing the at least one user interface to one or more users;receiving user feedback associated with the at least one user interface,wherein the user feedback includes data corresponding to userinteraction with the at least one user interface; and training at leastone of the one or more machine learning engines based on the datacorresponding to the user interaction with the at least one userinterface.
 12. The non-transitory computer-readable medium of claim 11,wherein the automatically generating the at least one user interfacecomprises: automatically selecting a plurality of suggested componenttypes for a subset of the data set; wherein the user feedback indicatesone of the plurality of suggested component types.
 13. Thenon-transitory computer-readable medium of claim 12, wherein theoperations further comprise: automatically generating, for the pluralityof suggested component types, a corresponding plurality ofrepresentative components; causing display of a particular userinterface that includes the corresponding plurality of representativecomponents, wherein each of the corresponding plurality ofrepresentative components depicts the subset of the data set; andreceiving input indicating a selected component type of the plurality ofsuggested component types, wherein the at least one user interface,provided to the one or more users, includes at least a portion of thedata set depicted using the selected component type.
 14. (canceled) 15.The non-transitory computer-readable medium of claim 11, wherein thedata corresponding to the user interaction includes at least one ofclick data for one or more user interface element included in the atleast one user interface, or hover data for one or more user interfaceelement included in the at least one user interface.
 16. Thenon-transitory computer-readable medium of claim 11, wherein the userfeedback includes data corresponding to user interaction, with the atleast one user interface, of a particular user, wherein the operationsfurther comprise: selectively training at least one of the one or moremachine learning engines based on the data corresponding to the userinteraction of the particular user.
 17. The non-transitorycomputer-readable medium of claim 11, wherein the user feedback includesdata corresponding to user interaction, with the at least one userinterface, of a particular set of one or more users, wherein theoperations further comprise: based on the data corresponding to the userinteraction of the particular set of one or more users, generating aprofile for the particular set of users, wherein the profile is used asan input for at least one of the one or more machine learning engines.18. A system, comprising: at least one processor; and a non-transitory,computer-readable medium having instructions stored thereon that areexecutable by the at least one processor to cause the system to performoperations, the operations comprising: storing template information thatdefines a plurality of component types and one or more displayparameters identified for one or more user interfaces, wherein acomponent type specifies a set of one or more user interface elementtypes included in a given component, and wherein a display parameterspecifies how one or more components are to be displayed; receiving arequest to automatically generate a user interface, wherein the requestspecifies a data set to be displayed using the user interface;automatically generating at least one user interface in response to therequest, wherein the generating is performed by one or more machinelearning engines that use the template information and the data set asinputs; providing the at least one user interface to one or more users;receiving user feedback associated with the at least one user interface,wherein the user feedback includes data corresponding to userinteraction with the at least one user interface; and training at leastone of the one or more machine learning engines based on the datacorresponding to the user interaction with the at least one userinterface.
 19. The system of claim 18, wherein the automaticallygenerating the at least one user interface comprises: automaticallyselecting a plurality of suggested component types for a subset of thedata set; wherein the user feedback indicates one of the plurality ofsuggested component types.
 20. (canceled)